SUMMARY / ABSTRACT Aortic Valve Disease can result in multi-factorial complications including alerted post-valvular 3D blood flow patterns and severe secondary aortopathy (aortic dilatation, aneurysm, and dissection). The current standard- of-care, however, assesses aortic valve disease severity and thus therapy management (surgery vs. conservative management) based on simplified measurements local to the valve. Paradoxically, it is well known that similarly classified aortic valve disease patients, exhibit radically divergent clinical presentations and outcomes. Evidence-based imaging biomarkers beyond aortic diameter capable of risk stratification are thus urgently needed. 4D flow studies have shown that the aortic valve disease phenotype has a strong effect on changes in aortic hemodynamics. Over the past years, we have assembled one of the largest aortic 4D MRI databases worldwide with over 1300 patient exams in patients with aortic valve disease (among these: >880 BAV, >420 with TAV). Also, we have established a large healthy aging cohort across a broad range of ages (n=189 controls free of cardiovascular disease, 20-40 per age decade: 20-30, 31-40, 41-50, 51-60, 61-70 years) and well distributed between genders (83 male, 106 female). However, 4D flow analysis across large cohorts has been hindered by large data sets (4000-6000 images per patient), cumbersome manual analysis limiting reducibility, and lack of exploitation of the comprehensive hemodynamic information (3D + time + 3-direction flow). To address these limitations, we have recently developed a novel non-invasive 4D virtual Catheter (vCath) technique that uses mathematical modeling to mimic the well-established invasive catheter in quantifying hemodynamics. 4D vCath utilizes the full 4D flow MRI information for flexible quantification of aortic 3D hemodynamic with high degree of automation. An advantage of the 4D vCath concept over existing analysis methods is rated to its intrinsic ability to simultaneously probe different basic (flow, peak velocity) and advanced (kinetic energy KE, viscous energy loss EL, vorticity) hemodynamic factors along the entire thoracic aorta. Our large cohort coupled with comprehensive 4D vCath analysis enables a unique opportunity to conduct a well-powered retrospective study to identify hemodynamic factors associated with aortopathy development. This project will develop new multi-parametric hemodynamic-based aortopathy risk factors, which will provide novel insights into aortopathy disease mechanisms and inform subsequent longitudinal outcome studies.